Early generation of individuals to accelerate genetic algorithms

ABSTRACT

While at least one candidate solution of a first generation of candidate solutions remains to be evaluated in accordance with a fitness function for an optimization problem, a plurality of candidate solutions is selected from the first generation of candidate solutions to participate in a tournament. It is determined whether each of the plurality of candidate solutions selected to participate in the tournament have been evaluated in accordance with the fitness function. If all have been evaluated, then one or more winners of the tournament are selected from the plurality of candidate solutions of the first generation of candidate solutions. A candidate solution of a second generation of candidate solutions is created with the selected one or more winners of the tournament in accordance with a genetic operator.

BACKGROUND

Embodiments of the inventive subject matter generally relate to thefield of computing, and, more particularly, to metaheuristicoptimization.

Software tools employ metaheuristic optimization algorithms to solveoptimization problems. Examples of metaheuristic optimization algorithmsinclude evolutionary algorithms (e.g., genetic algorithm, differentialevolution), ant colony optimization algorithms, simulated annealingalgorithms, etc.

Evolutionary algorithms use techniques loosely based on Darwinianevolution and biological mechanisms to evolve solutions to designproblems. A software tool that implements an evolutionary algorithmstarts with a randomly generated population of solutions, anditeratively uses recombination, crossover, mutation, and the Darwinianprinciples of natural selection to create new, more fit solutions insuccessive generations. Evolutionary algorithms have been deployed inmany aspects of research and development, and have generatedhuman-competitive solutions to a wide range of problems.

SUMMARY

Embodiments of the inventive subject matter include early constructionof candidate solutions. While at least one candidate solution of a firstgeneration of candidate solutions remains to be evaluated in accordancewith a fitness function for an optimization problem, a plurality ofcandidate solutions is selected from the first generation of candidatesolutions to participate in a tournament. It is determined whether eachof the plurality of candidate solutions selected to participate in thetournament have been evaluated in accordance with the fitness function.If all have been evaluated, then one or more winners of the tournamentare selected from the plurality of candidate solutions of the firstgeneration of candidate solutions. A candidate solution of a secondgeneration of candidate solutions is created with the selected one ormore winners of the tournament in accordance with a genetic operator.

BRIEF DESCRIPTION OF THE DRAWINGS

The present embodiments may be better understood, and numerous objects,features, and advantages made apparent to those skilled in the art byreferencing the accompanying drawings.

FIG. 1 depicts a conceptual diagram of an example genetic algorithmprocess with accelerated individual construction.

FIG. 2 depicts a flowchart of example operations for early constructionof individuals in an evolutionary process.

FIGS. 3-4 depict flowcharts of example operations for early constructionof individuals in an evolutionary process with PRNG seeding.

FIGS. 5-6 depict flowcharts of example operations that advance to latergenerations for early construction with seed manipulation.

FIG. 7 depicts a flowchart of example operations for a managing entityto govern early construction.

FIG. 8 depicts an example computer system with an evolutionary algorithmunit that implements early construction.

FIG. 9 depicts an example network of nodes carrying out a multi-dememodel of an evolutionary process with early construction.

FIG. 10 depicts three nodes responsible for local demes.

DESCRIPTION OF EMBODIMENT(S)

The description that follows includes exemplary systems, methods,techniques, instruction sequences and computer program products thatembody techniques of the present inventive subject matter. However, itis understood that the described embodiments may be practiced withoutthese specific details. In other instances, well-known instructioninstances, protocols, structures and techniques have not been shown indetail in order not to obfuscate the description.

Terminology

Literature about evolutionary computing uses a large variety ofterminology. In some cases, terms are used ambiguously. A “candidatesolution” can be program code, a data structure of actual parameters(e.g., vector or array of actual parameters), or both. In thisdescription, a candidate solution is used to refer to a data structureof actual parameters. This description uses the terms “formalparameters” and “actual parameters” instead of variable and argument.For example, a formal parameter is signal delay and the correspondingactual parameter may be 5 microseconds. A candidate solution is alsoreferred to as an individual.

Genetic algorithm literature sometimes uses the terms “population” and“generation” ambiguously. A software tool or computer program thatimplements an evolutionary algorithm to solve an optimization problemiteratively creates candidate solutions, computes fitness values for thecandidate solutions, and evaluates the candidate solutions to determinewhether a next iteration should begin. This description sometimes referto the aggregate of computing fitness value for a candidate solution andevaluating the computed fitness value as evaluating fitness of thecandidate solution. The candidate solutions created at a particulariteration are referred to as a generation of candidate solutions orgeneration, a population of candidate solutions, or a population ofcandidate solutions of a generation.

An evolutionary simulation or run may operate on a single deme model ora multi-deme model. A deme refers to a local population unit. In amulti-deme model, a population of candidate solutions is separated intosub-populations. Candidate solutions do not mix between demes ofsub-populations unless migration is implemented. When separated intosub-populations, the term “generation” typically refers to all of thesub-populations in that generation. The specification will sometimes usethe term “sub-population generation” to refer to a generation of one ofthe sub-populations. The term “population” also refers to the aggregateof candidate solutions across generations. Similarly, the term“sub-population” refers to the aggregate of candidate solutions managedby a deme manager across generations.

The specification will also use the term “iteration.” Iteration is usedto refer to a stage in the evolutionary algorithm based computingprocess, instead of generation. The term “process” is often used torefer to an instantiation of a sequence of machine-readable programinstructions. The term “evolutionary algorithm process” and“evolutionary process” are used herein to refer to a process(es) thatimplements an evolutionary algorithm (e.g., a genetic algorithm). Theterm “evolutionary algorithm process” is not limited to a singleinstance of executing instructions. The term “genetic algorithm process”is also used in the same manner as “evolutionary algorithm process.” Theterm “node” or “computing node” is used herein to refer to a computingresource unit. Examples of a node include a computer, a mobile device, avirtual machine, a core in a multi-core environment, a processor in amulti-processor environment, a group of computers in a cluster, a groupof computers in a grid, etc.

Finally, this description uses a semantic that refers to a tournamentbeing conducted in a source generation. For instance, tournamentparticipants are selected from generation N for early construction of anindividual or candidate solution in generation M. The tournament isdescribed as being conducted in generation N for early construction ofan individual in generation M. The parameters are maintained in theexample illustrations in accordance with this semantic. This semanticshould not have any impact on embodiments or claim scope.

Introduction

Efficient generation of candidate solutions can improve performance ofan evolutionary algorithm program. Conventionally, an entire generationis evaluated utilizing a fitness function before candidate solutions aregenerated for the next generation. Fitness of the candidate solutions ofa current generation are evaluated with a fitness function, and thecandidate solutions are ranked according to fitness. This creates anefficiency issue when the fitness function is complex because theadditional time for fitness evaluation also increases the time untilcandidate solutions can be constructed for the next generation. Possiblehardware and software failures can make the problem acute becausehardware and software failures may incur retries in fitness evaluation.The entire evolutionary process is held up until the very lastindividual of the population or sub-population has been evaluated. Inaddition, individual variations in candidate solutions lead to varyingtimes for fitness evaluation. In the case of a single deme model, thetime per generation is the maximum time to evaluate any one individual.In the case of a multi-deme model, the time per generation persub-population is the maximum time to evaluate any one individual withinthe corresponding deme. Although there is some tolerance of individualvariations because other demes may advance to the next generation, thetime for the evolutionary computing process to complete N generations isno less than the time for the slowest of the demes to complete Ngenerations.

To reduce the time per generation, candidate solution construction canbegin while a current generation is still under evaluation. Candidatesolutions from the generation undergoing evaluation are selected usingtournament selection for incremental construction of candidate solutionsfor a next generation. If candidate solutions from the generationundergoing evaluation are available to conduct the tournament, a winneror winners of the tournament are used to construct a candidate solutionfor the next generation. This early construction (or acceleratedconstruction) prevents a small number of current generation candidatesolutions from stalling the construction of the next generation.Although this early construction technique speculates that theevolutionary process will not terminate with the current generation, theearly construction does not speculate with respect to fitness. Sincetournament participants are selected based on actual fitness instead ofspeculative fitness, resources are not wasted evaluating erroneouscandidate solutions constructed based on mis-predictions about fitness,and rollback is avoided.

FIG. 1 depicts a conceptual diagram of an example genetic algorithmprocess with accelerated individual construction. The conceptual diagramdepicts a solution selector and propagator 103 and a fitness evaluator105. These represent processes that carry out different aspects of thegenetic algorithm process. The fitness evaluator 105 applies a fitnessfunction to each individual, and generates a fitness value thatrepresents fitness of the individual. The metric and form of the fitnessvalue can vary based on any one of the optimization problem, design ofthe genetic algorithm process, etc. In this example, the fitness valuesare expressed as decimal numbers that can be interpreted as degree offitness from 0 to 100%. The solution selector and propagator 103 selectsindividuals to participate in a tournament, and creates individualsbased on the tournament outcome. The solution selector and propagator103 and the fitness evaluator 105 are likely, but not necessarily,hosted on different computing nodes.

The solution selector and propagator 103 and the fitness evaluator 105operate upon example data structures 107, 109. Each of the datastructures 107, 109 correspond to a different generation. The structure107 corresponds to a generation N and the structure 109 corresponds to ageneration N+1. Each of the structures 107, 109 include three columns.Each entry in a first column indicates state of an individual. For thisillustration, the values 0-3 represent evaluation state as follows:

-   0=Not Constructed-   1=Constructed, Not Evaluated-   2=Under Evaluation-   3=Evaluated    Additional states can be maintained to indicate, for example,    whether the individual failed the fitness evaluation. Each entry in    a second column of the structures indicates fitness values generated    from the fitness evaluation. Each entry in a third column contains a    reference or pointer to an individual. Entries in the third column    of the data structure 107 reference individuals in generation N.    Entries in the third column of the data structure 109 reference    individuals in generation N+1. In this example illustration, all    individuals of generation N have been constructed, and generation N    is the generation currently under evaluation.

In FIG. 1, several stages are depicted. The different stages are used todelineate different operations, and are not to be used to limit anyembodiments to the particular order illustrated. Furthermore, the stagesare a snapshot of example operations that occur during an evolutionaryprocess. For this illustration, a stage A is depicted for the fitnessevaluator 105. FIG. 1 depicts stages B.1 through B.4 for the solutionselector and propagator 103.

At stage A, the fitness evaluator 105 sets state for an individual 119in generation N to indicate “Under Evaluation,” and begins evaluatingfitness of the individual 119. Setting state, however, may be performedby a process or node different than the process or node that performsthe fitness evaluation. A process or node that has access to thestructure 107 can set the state to indicate “Under Evaluation” and thensend the individual 119 or a reference to the individual 119 to the nodeor process that actually performs the fitness evaluation.

Over the series of B stages, the solution selector and propagator(hereinafter “propagator”) 103 conducts a tournament and constructs anindividual of generation N+1. At stage B.1, the propagator 103 selectstournament participants for a tournament. The propagator 103 randomlyselects tournament participants from generation N. In this illustration,individuals 0, 2, 3, and 5 are selected for the tournament. At stageB.1, the propagator 103 ensures that all of the selected participantshave been evaluated. The propagator 103 reads the evaluation state andverifies that all participants have a state of “Evaluated.” In somecases, all selected tournament participants may not be evaluated. If anyone of the selected tournament participants is not evaluated, then thetournament is not conducted. Since all of the selected tournamentparticipants have been evaluated, the propagator 103 determines twowinners of the tournament at stage B.3. The winners of the tournamentare individuals 115, 117. Within the tournament, the individual 115 hasthe highest fitness value of 0.93, and the individual 117 has the secondhighest fitness value of 0.89. Although this example presumes twowinners are selected from a tournament, embodiments can vary. Atournament may only have a single winner. If the genetic operatorrequires multiple individuals (e.g., crossover), then a tournament canbe conducted for each required individual. At stage B.4, the propagator103 applies the genetic operator to the individuals 115, 117 topropagate their genes to generation N+1 while generation N is stillunder evaluation. The propagator 103 constructs individual 113 ingeneration N+1 from applying the genetic operator to individual 115 andindividual 117.

As noted in the description of FIG. 1, embodiments may not conduct atournament when a tournament participant has not yet been evaluated.When a tournament cannot be conducted because an individual has not beenevaluated, embodiments can wait until the participant is evaluated, oradvance to another future generation. In either case, the random numbersused for selecting the tournament participants can be stored ordiscarded. If discarded, perhaps to conserve resources, repeatability ispreserved by incorporating an indication of the generation into aseed(s) for a pseudo-random number generator (PRNG). The seed is used toensure the same random numbers are generated for a particulargeneration. A typical seed is used to initialize internal state of aPRNG. Typically, a PRNG initializes an internal state based on a seedand updates that state over time. To ensure the same sequence of randomnumbers is generated per generation, the seed incorporates an indicationof the generation so that each generation has a unique seed. In the caseof a multiple deme model, each deme per generation has a unique seed.The seed per deme per generation (deme_gen_seed) could be computed inaccordance with the following function:deme_gen_seed=(G*N+D)+S,where G corresponds to generation, N corresponds to number of demes, Dcorresponds to a deme, and S corresponds to an initial random seed.Additional variations are possible for distinguishing seeds, for exampleper individual. Variations of functions to compute the seed toinitialize the PRNG are also possible as long as seeds to initialize thePRNG are unique across generations per population unit and preserverepeatability.

FIG. 2 depicts a flowchart of example operations for early constructionof individuals in an evolutionary process.

At block 201, a plurality of candidate solutions are selected from ageneration N to participate in a tournament for a candidate solution Xof generation N+1. The participants are selected while there is at leastone candidate solution to be evaluated in generation N. Adesigner/developer of the evolutionary program defines the number ofparticipants of a tournament in dependence upon the biological operationused for creating candidate solution X. For instance, the number ofparticipants should be at least 2 if the biological operation iscrossover.

At block 203, it is determined if all of the selected candidatesolutions can participate in the tournament. A candidate solution thathas failed evaluation cannot participate in the tournament. In addition,a candidate solution may not be viable (e.g., the candidate solution maynot be acceptable input for a simulator). If the tournament cannot beconducted with the selected participants, then control flows to block205. Otherwise, control flows to block 207.

At block 205, an indication of the failed tournament is returned. Thefailed tournament indication notifies the process (e.g., a deme manager)that requested the early construction that the tournament could not beconducted. The deme manager can then wait to request early constructionof individual X again.

At block 207, the tournament is conducted and a winner of the tournamentis determined. Although this flowchart of example operations presumes atournament designed to produce a single winner, embodiments can conducta tournament that produces multiple winners, perhaps winners sufficientto create the individual to be constructed early.

At block 209, it is determined if candidate solution X of generation N+1can be created. The early construction process determined whether thetournaments have yielded enough candidate solution of generation N tocreate individual X in accordance with the corresponding biologicaloperation. If sufficient winners have not been yielded, then controlflows to block 211. If sufficient winners have been yielded from thetournaments, then control flows to block 213.

At block 213, individual X is created from the tournament winners.

At block 215, a counter X is incremented to indicate a next individualof generation N+1 for early construction. Control flows from block 215back to block 201. Embodiments may allow the early construction processto continue until interrupted by, for example, a deme manager orevolutionary process manager, or embodiments may return to the callingprocess (e.g., deme manager) after early construction of a given numberof individuals.

If the individual X could not yet be constructed at block 209, thenanother plurality of candidate solutions is selected from generation Nat block 211 for another tournament. Control flows from block 211 backto block 203.

Selection of candidate solutions to participate in tournaments is donein a repeatable or reproducible manner in case the tournament fails.Random selection of candidate solutions as tournament participants canbe reproducible by manipulating seeding of a random number generator.FIGS. 3-4 depict flowcharts of example operations for early constructionof individuals in an evolutionary process with PRNG seeding.

At block 301, an initial generation of candidate solutions is created.For example, random candidate solutions can be generated as an initialpopulation. The initial population may or may not include seed candidatesolutions. Formal parameters N and M are used to track the generationunder evaluation and an advanced generation being constructed early. Theformal parameter N is initialized to 0 to represent the initialgeneration, and the formal parameter M is initialized to N+1.

At block 303, generation N is submitted for fitness evaluation. Forexample, an index to a structure that identifies all candidate solutionsof generation N is passed to a process that computes the fitness valuesand evaluates the fitness values against termination criteria.Embodiments may pass an actual candidate solution or a reference to acandidate solution to the process or node carrying out fitnessevaluation.

At block 305, it is determined whether M is greater than a maximum run.In case the termination criterion is not satisfied in a maximum numberof permitted runs, the evolutionary process can be limited to themaximum number of permitted runs. If M is greater than the maximumnumber of allowable runs, then control flows to bock 307. If M is notgreater than the maximum number of allowable runs, then control flows toblock 311.

At block 307, the evolutionary process waits until fitness evaluationcompletes for generation N. After the fitness evaluation completes forgeneration N, the result is output at block 308.

If the maximum run will not be exceeded by generation M, then controlflows to block 311. At block 311, it is determined whether an acceleratethreshold has been met for generation M. An accelerate threshold definesa threshold number of candidate solutions to have been evaluated ingeneration N before early construction can begin for generation M. Forexample, a designer may define the accelerate threshold to be a functionof the population size and/or tournament size (e.g., χtimes tournamentsize or χ% of population size). If the accelerate threshold is not metfor generation M, then the evolutionary processes waits at block 313.The evolutionary process can wait for any one of a number of cycles, atime period, a number of candidate solutions to be evaluated, etc.Control returns to block 311 from block 313.

If the accelerate threshold is met for generation M, then a trackingstructure is instantiated for generation M at block 315. A variety ofstructures can be realized that track generation M. The structure caninclude or reference the candidate solutions generated for generation M.The structure also indicates state and fitness of the candidatesolutions in generation M. Example structures are depicted in FIG. 1,although the structures in FIG. 1 are not intended to limit the possibleembodiments or implementations.

At block 319, a generation seed for a PRNG is computed with a generationvalue that indicates or corresponds to generation N and an initial stateseed. A variety of functions are possible for computing the generationseed that incorporate an indication of the generation for which therandom numbers will be generated.

At block 321, the PRNG is seeded with the generation seed. Theevolutionary process passes the generation seed to afunction/method/procedure that initializes the PRNG with the generationseed.

At block 323, a genetic operator is selected and a number of tournamentparticipants (X) is determined based on the genetic operator. In somecases, an evolutionary process may use a different genetic operator atcertain iterations or even within an iteration. Different geneticoperators operate upon a different number of individuals. For instance,crossover involves at least two individuals and mutation involves asingle individual. A designer can define a tournament size as a functionof the number of individuals needed for a genetic operator. Thus,tournament size may vary per individual. Control flows to block 401 ofFIG. 4 from block 323.

At block 401, X random numbers are obtained from the PRNG. The randomnumbers can be obtained in accordance with a variety of techniquesdepending upon how the PRNG and the evolutionary process are implemented(e.g., X individual requests, a single request for X numbers, Xcalls/invocations of the PRNG, etc.).

At block 403, it is determined whether the candidate solutions ingeneration N corresponding to the obtained random numbers have beenevaluated. If the candidate solutions have been evaluated, then controlflows to block 407. Otherwise, control flows to block 405 to wait beforecontrol flows back to block 403. The part of the evolutionary processresponsible for conducting the tournaments (“tournament process”) waitsfor a configured amount of time, cycles, etc. Embodiments may alsoconfigure tournament program code to post a request to the fitnessevaluator for notification when a given number of additional candidatesolutions have completed fitness evaluation. Embodiments may alsoconfigure the fitness evaluator to independently notify the processresponsible for tournaments and/or early construction each time a givennumber of candidate solutions complete fitness evaluation. Thetournament/early construction process can count the notifications todetermine whether a threshold number of candidate solutions have beenevaluated to retry a tournament. As described earlier, a PRNG maintainsan internal state which allows repeatability in sequence of randomnumbers generated after initialization or seeding. If all participantsfor a tournament are not evaluated and a tournament is later retried,the same random numbers will be obtained to ensure that the sameparticipants are selected. Embodiments may choose to store randomnumbers along with indication of the corresponding generation andindividual instead of running the PRNG again.

At block 407, winners of the tournament are selected.

At block 409, the candidate solution for generation M is constructedbased on the winners of the tournaments and the genetic operator.

At block 410, it is determined whether the candidate solution is uniquewithin generation M. If the candidate solution is not unique, then thecandidate solution may be discarded and the tournament retried with adifferent set of random numbers. Thus, control flows back to block 401if the candidate solution constructed at block 409 is not unique withingeneration M. If the early constructed candidate solution is uniquewithin generation M, then control flows to block 411.

At block 411, the tracking structure for generation M is updated toindicate the constructed individual. For example, the tracking structureis populated with state of the constructed candidate solution (e.g.,Constructed, Not Evaluated), and a reference to the constructedcandidate solution. In addition, a counter of constructed individualscan be incremented.

At block 413, a completion check for generation Nis performed.

At block 414, the constructed candidate solution is submitted forfitness evaluation. The constructed candidate solution may be placed ina queue of candidate solutions to be evaluated. The constructedcandidate solution may be inserted directed into a queue, via messaging,by reference, etc. The constructed candidate solution may also be markedas early construction. If the evolutionary process allows fitnessevaluation to be performed in parallel with determining whether atermination criterion has been satisfied, then the indication of earlyconstruction can guide the fitness evaluator to ensure the terminationcriterion has not been satisfied before evaluating early constructioncandidate solutions.

At block 415, it is determined whether generation M is complete. Ifgeneration M is not complete, then control flows back to block 323. Ifgeneration M is complete, then control flows to block 417.

At block 417, N is set to M and M is incremented. The update inparameters moves early construction to the next generation. The changein parameters N and M occur since generation M could not be completelyconstructed unless fitness evaluation completed for generation N.Control flows from block 417 to block 305.

Although the example operations of FIGS. 3-4 wait when a tournamentcannot be conducted, embodiments are not so limited. Without usingspeculative individuals or speculative fitness values, the earlyconstruction can continue advancing to later generations. In otherwords, multiple future generations can be under construction while ageneration is still under evaluation. FIGS. 5-6 depict flowcharts ofexample operations that advance to later generations for earlyconstruction with seed manipulation.

At block 501, an initial generation of candidate solutions is created.In addition, formal parameters are initialized. UNDER_EVALUATIONindicates the generation currently under evaluation. The formalparameters M and N are used to track generations involved with earlyconstruction. M is used in this example flowchart to track thegeneration being constructed early, and N is used in this exampleflowchart to track the generation sourcing candidate solutions forgeneration M. The formal parameters UNDER_EVALUATION and N areinitialized to 0 corresponding to the initial generation. The formalparameter M is initialized to N+1. A structure of single bit flagsSKIP[] (e.g., an array) of size MAX_RUN is initialized to FALSE or 0.This flag is used to mark when early construction has commenced for ageneration and skipped to another generation because all tournamentparticipants were not available (i.e., fitness evaluation not yetcomplete).

At block 503, the generation N is submitted for fitness evaluation.

At block 505, it is determined whether M is greater than a maximum run.In case the termination criterion is not satisfied in a maximum numberof permitted runs, the evolutionary process can be limited to themaximum number of permitted runs. If M is greater than the maximumnumber of allowable runs, then control flows to bock 507. If M is notgreater than the maximum number of allowable runs, then control flows toblock 515.

At block 507, the evolutionary process waits until fitness evaluationcompletes for generation N. After the fitness evaluation completes forgeneration N, the result is output at block 508.

If the maximum run or iteration will not be exceeded by generation M,then control flows to block 515. At block 515, it is determined whetheran accelerate threshold is met for generation N. As described in theprevious flowcharts, the accelerate threshold can be configured inaccordance with different techniques. If the accelerate threshold hasnot been met, then control flows to block 511. If the acceleratethreshold is met, then control flows to block 517.

At block 511, parameters are set to indicate that the current generationunder evaluation is sourcing the candidate solutions for the generationbeing constructed early. As long as the generation under evaluation isthe initial generation, the parameters N and M are effectively beingreinitialized to the same values. An implementation can simply checkwhether the generation UNDER_EVALUTION is still generation 0. If so,block 511 can be avoided. In later iterations, though, the setting ofparameters in block 511 brings early construction back to the currentgeneration from a later generation that is more than one iteration awaywhen the accelerate threshold has not been met for that latergeneration.

At block 513, the process waits for a given time period or candidatesolutions to try early construction again. Control flows from block 513back to block 515. Implementations may not perform the wait at block 513once the evolutionary process (or a deme) has progressed to a generationbeyond early generations, such as generation 0. Instead of waiting,early construction will have advanced to later generations whenpossible.

If the accelerate threshold was met for generation N at block 515, thenit is determined at block 517 whether the SKIP flag indicates TRUE orFALSE. If the flag indicates TRUE, then control flows to block 525. Ifthe flag indicates FALSE, then control flows to block 518.

At block 518, a tracking structure is instantiated for generation M. Asdescribed with respect to block 315, a variety of structures can berealized that track generation M. The structure can include or referencethe candidate solutions generated for generation M. The structure alsoindicates state and fitness of the candidate solutions in generation M.In addition, the following initializations are performed: a parameter Zis initialized to 0; a parameter RANDOM[N][Z] is initialized to 0; and aparameter CANDIDATE[M] is initialized to 0. For this exampleillustration, Z is used to track which tournaments have been conductedfor a generation N. The parameter RANDOM[N][Z] is used to track thenumber of random numbers generated for tournament Z in generation N. Theparameter CANDIDATE[M] is used to identify which candidate in generationM is being early constructed.

At block 519, a generation seed for a PRNG is computed with a generationvalue that indicates or corresponds to generation N and an initial stateseed. A variety of functions are possible for computing the generationseed that incorporate an indication of the generation for which therandom numbers will be generated.

At block 521, the PRNG is seeded with the generation seed. Theevolutionary process passes the generation seed to afunction/method/procedure that initializes the PRNG with the generationseed.

At block 523, a genetic operator is selected for CANDIDATE[M] and anumber of tournament participants (X) is determined based on the geneticoperator. As described with respect to block 323 earlier, tournamentsize can vary per individual based on the genetic operator for thatindividual. Control flows to block 601 of FIG. 6 from block 523.

At block 601, X random numbers are obtained from the PRNG. The randomnumbers can be obtained in accordance with a variety of techniquesdepending upon how the PRNG and the evolutionary process are implemented(e.g., X individual requests, a single request for X numbers, Xcalls/invocations of the PRNG, etc.). The parameter RANDOM[N] [Z] isupdated to indicate the number of random numbers generation fortournament Z in generation N (RANDOM[N][Z]=RANDOM[N][Z]+X).

At block 603, it is determined whether the candidate solutions ingeneration N corresponding to the obtained random numbers have beenevaluated. If the candidate solutions have been evaluated, then controlflows to block 607. Otherwise, control flows to block 605.

At block 607, the tournament tracker parameter Z is incremented and awinner or winners of the tournament are selected.

At block 609, the candidate solution for generation M is constructedbased on the winners of the tournament and the genetic operator.

At block 610, it is determined whether the candidate solution is uniquewithin generation M. If the candidate solution is not unique, then thecandidate solution may be discarded and the tournament retried with adifferent set of random numbers. Thus, control flows back to block 601if the candidate solution constructed at block 609 is not unique withingeneration M. If the early constructed candidate solution is uniquewithin generation M, then control flows to block 611.

At block 611, the tracking structure for generation M is updated toindicate the constructed candidate solution. For example, the trackingstructure is populated with state of the constructed candidate solution(e.g., Constructed, Not Evaluated), and a reference to the constructedcandidate solution. In addition, the counter of constructed individualsin generation M CANDIDATE[M] is incremented.

At block 613, a completion check for generation UNDER_EVALUATION isperformed.

At block 614, the constructed candidate solution is submitted forfitness evaluation. The variety of techniques for submitting a candidatesolution as described with respect to block 314 is similarly applicablefor block 614.

At block 615, it is determined whether generation M is complete. Ifgeneration M is not complete, then control flows back to block 523. Ifgeneration M is complete, then control flows to block 617.

At block 617, the parameters M and N are updated to track advancement ofearly construction (N=M; M++). Control flows from block 617 to block 505of FIG. 5.

If it was determined at block 603 that all tournament participants hadnot been evaluated yet, then control flowed to block 605. At block 605,parameters are updated to reflect that early construction will advanceto a next generation and early construction is not yet complete forgeneration M. A parameter TOURNAMENTS[N], which tracks tournamentsconducted in a generation N, is set to Z. The tournament counter Z isreset to 0, and the SKIP[M] flag is set to TRUE to indicate that earlyconstruction is skipping ahead without completing construction atgeneration M. Control flows from block 605 to block 617.

Returning to block 517 where the SKIP[M] flag is evaluated, the SKIPflag will have been set to TRUE at block 605 because early constructionskipped ahead without the generation under construction completing. Ifthe SKIP[M] flag evaluates to TRUE, then control flows to block 525where operations begin to restore state of the PRNG to the lasttournament that failed (or was not conducted) in generation N.

At block 525, the PRNG is seeded with the previously computed seedGENERATION_SEED[N].

At block 527, a loop is run to generate the random numbers from the PRNGfor each tournament that was previously conducted successfully ingeneration N. A loop control variable Z iterates from 0 to the number ofconducted tournaments as represented by the parameter TOURNAMENTS[N]−1.The loop runs to one less than the number of tournaments because thisexample flowchart counts the last failed tournament in generation N theparameter TOURNAMENTS[N]. For each tournament that was conducted, anumber of random numbers represented by the parameter RANDOM[N][Z] isobtained. This parameter indicates the number of random numbersgeneration for tournament Z in generation N.

At block 529, the genetic operator is selected for a candidate indicatedby the parameter CANDIDATE[M], and random numbers amounting to a numberindicated by the parameter RANDOM[N][TOURNAMENTS[N]] are obtained.Control flows from block 529 to block 603 of FIG. 6.

Although FIGS. 5-6 integrate early construction with managing earlyconstruction, embodiments can separate the management from the earlyconstruction. A deme manager or global process manager can track stateof demes and generations, including a number of evaluated candidatesolutions in each deme of each generation. In that case, the managingentity can invoke an early construction function that accepts anindication of generation and deme as actual parameters. The earlyconstruction function can then proceed to the next unconstructedindividual of the indicated deme in the indicated generation. Or themanaging entity can specify individuals to be constructed. Whenresponsibilities are split in this manner, the early constructionfunction can return a value that indicates a tournament cannot beconducted or a value that indicates a threshold (e.g., acceleratethreshold) is not satisfied instead of waiting. The managing entitygoverns when the early construction function can begin work again andthe target deme and generation. FIG. 7 depicts a flowchart of exampleoperations for a managing entity to govern early construction.

At block 701, early construction is requested in a generation.

At block 703, it is determined if the early construction was successful.If the tournament could not be conducted, then the early constructionwill not be successful. In some implementation, the managing process(e.g., deme manager) can determine whether a deme in a particulargeneration is eligible for early construction before requesting earlyconstruction (e.g., determining whether x individuals have beenevaluated in the target deme of the target generation). If earlyconstruction was successful, then control flows to block 705. If not,then control flows to block 709.

At block 705, it is determined whether there are additional individualsto construct in the generation. An early construction may construct onecandidate solution at a time or a predefined number of candidatesolutions per request. The number of candidate solutions to constructearly per request depends on a correspondence of potential resourcesbeing used for early construction of a generation beyond a terminationpoint. In some implementations, the early construction function cancontinue constructing individuals until a tournament fails. If there areadditional individuals to construct, then control flows to block 707.The managing process determines that the generation undergoing earlyconstruction is not yet complete, and again requests early constructionfor the generation at block 707.

If there were no additional individuals to construct early in thegeneration, then it is determined at block 709 if a later generation iseligible for early construction. A later generation can be eligible whena threshold number of candidate solutions in the preceding generationhave been evaluated. Embodiments can define other criteria foreligibility, such as limiting how far early construction can advanceahead. If a later generation is not eligible for early construction,then control flows to block 713. If a later generation is eligible forearly construction, then control flows to block 711.

At block 713, the managing process waits to request early constructionin an earlier generation when a later generation is not eligible. Insome cases, early construction may have begun in multiple generationsbeyond the generation currently undergoing evaluation. The managingprocess can wait for additional individuals to complete evaluation, andthen request early construction again.

At block 711, the later generation is eligible so early construction isrequested for the later generation. Control flows from block 711 toblock 703.

As noted above, early construction can be hindered when a tournamentparticipant has not been evaluated. Since the random numbers that guidetournament selection are repeatable, the random numbers can be generatedin advance to guide fitness evaluation order. The candidate solutionsthat will be selected for tournaments can be prioritized in accordancewith order of their selection for tournament participation. Embodimentscan define priority values for each candidate solution. Priority isgiven to candidate solutions of earlier generations, and then by orderof tournaments. Priority need not be defined between candidate solutionswithin a same tournament. A single priority value can incorporate thegeneration and a value that corresponds to when the candidate solutionis selected for a tournament. Greater weight can be given to thegeneration by setting aside the most significant bits of the priorityvalue for the generation, and less significant bits for the value thatcorresponds to tournament selection. FIG. 8 depicts a flowchart ofexample operations for assigning priority values to candidate solutionsto prioritize based on the random numbers that will guide tournamentselection.

At block 801, a loop begins to set priority for each candidate solutionof a generation. In this example, Z is the loop control variable. Theloop will repeat from START to THRESHOLD. These parameters are definedby the programmer/designer. START can be generation 1 or a latergeneration, based on a premise that Z represents the generation to beconstructed early and not the source generation. A programmer/designermay define THRESHOLD to be generation 8 to minimize resource consumptionfor computing priority. In addition, priority may be computed at lateriterations. For instance, priority may be computed for generations 0 to10, and then postponed until early construction begins to stall.

At block 803, a generation seed GENERATION_SEED[Z−1] is computed for aPRNG. As with the earlier flowcharts, the generation seed is computed asa function of an initial state seed and the source generation, which isgeneration Z−1 in this case although the semantics may vary as long asthe seeds are unique across generations.

At block 805, the PRIORITY parameter is set to a MAX_PRIORITY, whichrepresents a maximum boundary for priority. The developer of theevolutionary code or designer of the optimization problem can define themaximum priority value.

At block 807, another loop begins for each individual of generation Z.The loop control variable for this first nested loop is X. The loop willrepeat from 0 to N−1, where N is the size of the population. In amulti-deme model, N would be the size of a sub-population.

At block 809, the genetic operator is determined for constructing thecandidate GENERATION[Z].CANDIDATE[X].

At block 811, a number of participants, represented by the parameter P,in a tournament for GENERATION[Z].CANDIDATE[X] is determined.

At block 813, a second nested loop begins for each tournamentparticipant. For the second nested loop, the loop control variable is S.The second nested loop will repeat for each tournament participant from0 to P−1.

At block 815, a random number, represented by the parameter RN, isobtained from the PRNG.

At block 817, priority of the candidate solution of generation Z−1 thatcorresponds to the obtained random number is set. In this exampleillustration, the priority, represented by the parameterGENERATION[Z−1].CANDIDATE[RN].PRIORITY is set as a one's complement ofthe generation Z−1. The ones complement operator is used to give lowergeneration numbers greater priority. The priority is then bit shiftedover by half the length of the parameter. Finally, the value of PRIORITYis added to the parameter GENERATION[Z−1].CANDIDATE[RN].PRIORITY. Thesequence of operations is only an example of operations that can be usedto set priority to give greater weight to earlier generations and thento earlier selected tournament participants.

At block 819, it is determined whether all of the tournamentparticipants have been processed (S==P−1). If the second nested loopdoes not terminate, then control flows back to block 813. Otherwise,control flows to block 821.

At block 821, the PRIORITY parameter is decremented. Embodiments canmodify the PRIORITY in accordance with a variety of techniques todecrease priority for the next selected tournament participant.Embodiments can also assign the same priority to participants of thesame tournament. For example, block 821 may be performed before block819.

At block 823, the loop termination condition is evaluated for the firstnested loop, which iterates through each individual of generation Z−1(X==N−1). If the first nested loop does not terminate then control flowsback to block 807. Otherwise, control flows on to block 825.

At block 825, the outer loop termination condition is evaluated. If allgenerations to be prioritized as defined by the START and THRESHOLDparameters have been prioritized, then the flow ends. If the terminationcondition (Z==THRESHOLD) is not satisfied, then control flows back toblock 801.

Some Variations from Example Embodiments

The completion checks represented by FIG. 4 and FIG. 7 are not limitedto determining whether a termination criterion has been satisfied.Embodiments can insert additional checks to determine whether thegeneration under evaluation has completed evaluation, and determinewhether criteria for other disruptive actions (e.g., elitism, acataclysm, etc.) are satisfied. Disruptive actions may be deterministic,though. For example, the evolutionary process may be configured toimplement elitism at a particular generation or generations. In thatcase, early construction can be turned off for the particular generationor generations based on configuration information. In addition, aninterrupt mechanism can be implemented instead of or in addition toformal checks.

Other variations can arise with respect to when early construction isactivated or deactivated. FIG. 2 depicts conditioning activation ofgeneration acceleration or early construction upon a threshold number ofcandidate solution completing fitness evaluation. Embodiments may alsocondition early construction upon the evolutionary process achieving aparticular iteration. For instance, a designer can configure anevolutionary process to activate early construction when iteration 10 isachieved. Similarly, embodiments can deactivate early construction whenan iteration is achieved. For example, a designer can configure anevolutionary process to deactivate early construction when 80% of themaximum runs have been achieved.

It should be appreciated that the flowcharts above are examples to aidin understanding the inventive subject matter. Embodiments can performother operations, fewer operations, additional operations, operations ina different order, etc. For example, tournament size may not need to bedetermined as depicted in block 223 of FIG. 2 if the tournament sizeremains consistent throughout an evolutionary process. As anotherexample of variation, the operations to compute a generation seed andinitialize the PRNG as depicted in blocks 219 and 221 can be done by aseparate process in parallel with the operations depicted at blocks 215and 223.

Although the example Figures depict conducting a tournament of atournament size sufficient to satisfy input for a genetic operator,embodiments may implement a tournament to select less than the number ofinput individuals for a genetic operator. For instance, an embodimentmay select a single winner from each tournament. In this case, atournament is conducted to determine each input individual for a geneticoperator. If z tournaments are to be conducted for a genetic operatorthat takes z individuals as input and each tournament has a size s, thens*z random numbers will be obtained from the PRNG. Embodiments maychoose to obtain the s*z random numbers in a batch instead of pertournament under these circumstances, although these circumstances arenot a necessary condition for such an embodiment. If random numbers willbe obtained across tournaments, embodiments will preserve the sequenceof generation of the random numbers at least between tournaments. If agenetic operator for individual i of generation N takes p inputs, then ptournaments will be conducted for individual i. For a tournament size s,the PRNG will generate p*s random numbers. Although the order ofgeneration of the first s random numbers need not be preserved, thetournament/early construction process will ensure the random numbers ofthe each successive group of s random numbers is not used before thepreceding group or after any subsequent group. Preservation of orderbetween tournaments preserves repeatability of the tournaments.

Another possible variation in embodiments relates to balancing storageresources against compute resources. Some of the values (e.g., generatedrandom numbers, selected genetic operator for an individual, etc.) canbe stored in a history of information once determined, and recalled whenan early construction generation is revisited. Instead of running thePRNG again or re-executing the code that determines a genetic operator,for example, the information can be accessed. The volume of informationcan be large when working with a large population or numerous demes, sothe problem designer or programmer will determine which resources ismore scarce and choose an appropriate technique.

The example illustrations are presented in the context of a single dememodel. In the case of multiple demes, operations similar to the exampleoperations would be performed per deme. Early construction could advanceat different rates per deme, and a function to compute the PRNG seedswould incorporate an indication of the deme as well as population.Additional operations may be performed to accommodate migration betweendemes. Candidate solutions constructed early can be marked as earlyconstruction and/or marked with an indication of the generation. Ifmigration occurs for a generation, early construction can be implementedin different manners. If individuals migrate between demes in ageneration N, then early construction of individuals for generation N+1can be deactivated. Embodiments can also stop early construction atgeneration N+1 and delay sending early constructed individuals forfitness evaluation until the impact of migration is determined. If thenumber of migrants in generation N is configured to be m, then earlyconstruction can be stalled for any tournament with any one of the lastm individuals of generation N. In addition, the evolutionary process canbe designed or configured to implement migration last to allow otherindividuals to be early constructed beforehand. Further, a tournamentfor generation N+1 that includes an immigrant candidate solution canwait until the immigrant candidate solution arrives.

As will be appreciated by one skilled in the art, aspects of the presentinventive subject matter may be embodied as a system, method or computerprogram product. Accordingly, aspects of the present inventive subjectmatter may take the form of an entirely hardware embodiment, an entirelysoftware embodiment (including firmware, resident software, micro-code,etc.) or an embodiment combining software and hardware aspects that mayall generally be referred to herein as a “circuit,” “module” or“system.” Furthermore, aspects of the present inventive subject mattermay take the form of a computer program product embodied in one or morecomputer readable medium(s) having computer readable program codeembodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination of the foregoing. Morespecific examples (a non-exhaustive list) of the computer readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of theforegoing. In the context of this document, a computer readable storagemedium may be any tangible medium that can contain, or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent inventive subject matter may be written in any combination ofone or more programming languages, including an object orientedprogramming language such as Java, Smalltalk, C++ or the like andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The program codemay execute entirely on the user's computer, partly on the user'scomputer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider).

Aspects of the present inventive subject matter are described withreference to flowchart illustrations and/or block diagrams of methods,apparatus (systems) and computer program products according toembodiments of the inventive subject matter. It will be understood thateach block of the flowchart illustrations and/or block diagrams, andcombinations of blocks in the flowchart illustrations and/or blockdiagrams, can be implemented by computer program instructions. Thesecomputer program instructions may be provided to a processor of ageneral purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions, which execute via the processor of the computer orother programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

FIG. 9 depicts an example computer system with an evolutionary algorithmunit that implements early construction. A computer system includes aprocessor 901 (possibly including multiple processors, multiple cores,multiple nodes, and/or implementing multi-threading, etc.). The computersystem includes memory 907. The memory 907 may be system memory (e.g.,one or more of cache, SRAM, DRAM, zero capacitor RAM, Twin TransistorRAM, eDRAM, EDO RAM, DDR RAM, EEPROM, NRAM, RRAM, SONOS, PRAM, etc.) orany one or more of the above already described possible realizations ofmachine-readable media. The computer system also includes a bus 903(e.g., PCI, ISA, PCI-Express, HyperTransport® bus, InfiniBand® bus,NuBus bus, etc.), a network interface 905 (e.g., an ATM interface, anEthernet interface, a Frame Relay interface, SONET interface, wirelessinterface, etc.), and a storage device(s) 909 (e.g., optical storage,magnetic storage, solid state storage device, etc.). The computer system901 also comprises an evolutionary algorithm unit 925, which includes anearly construction unit 926. The evolutionary algorithm unit 925 mayimplement all aspects of an evolutionary process (e.g., candidategeneration, fitness evaluation, etc.) or a subset of the functionalityof an evolutionary process (e.g., candidate solution construction butnot fitness evaluation). In addition, the evolutionary algorithm unit925 may be assigned a local deme, carry candidate solution constructionfor that local deme, and report to a global manager that overseesmultiple demes. The early construction unit 926 carries out earlyconstruction of candidate solutions for a generation beyond theearliest/oldest generation undergoing fitness evaluation when tournamentparticipants are available from the source generation. Although depictedas a part of the evolutionary algorithm unit 925, the early constructionunit 926 can be implemented separately from the evolutionary algorithmunit 925. The early construction unit 926 may be partially (or entirely)implemented in hardware and/or on the processor 901. For example, thefunctionality may be implemented with an application specific integratedcircuit, in logic implemented in the processor 901, in a co-processor ona peripheral device or card, etc. Further, realizations may includefewer or additional components not illustrated in FIG. 9 (e.g., videocards, audio cards, additional network interfaces, peripheral devices,etc.). The processor 901, the storage device(s) 909, and the networkinterface 905 are coupled to the bus 903. Although illustrated as beingcoupled to the bus 903, the memory 907 may be coupled to the processorunit 901.

FIG. 10 depicts an example network of nodes carrying out a multi-dememodel of an evolutionary process with early construction. A network ofnodes 1000 is depicted with several nodes communicatively coupled via anetwork 1014, which can be implemented in accordance with any of avariety of network technologies. A node 1001 hosts a global evolutionaryprocess manager 1007. The evolutionary process manager 1007 allocatesand manages resources for the evolutionary process and monitors progressand results of local demes. A node 1003 hosts a fitness evaluator 1005.The fitness evaluator 1005 runs simulations and computes fitness values.

FIG. 10 depicts three nodes responsible for local demes. A node 1009hosts a deme manager 1015 and an early constructor 1017. A node 1011hosts a deme manager 1019 and an early constructor 1021. A node 1013hosts a deme manager 1023 and an early constructor 1025. The dememanagers each generate initial local demes or sub-populations, unlessassigned to them by the global evolutionary process manager 1007. Eachof the deme managers reports progress of their local deme to the globalevolutionary process manager periodically and/or in response to requestfrom the global evolutionary process manager 1007. Each of the earlyconstructors conduct tournaments for their respective local deme tobegin early construction of a next generation while a current generationawaits completion of fitness evaluation. Although each of the earlyconstructors can advance at their own rates dependent upon their localdemes fitness evaluation, the global evolutionary process manager 1007may stall an early construction to prevent any one deme from beginningearly construction for a generation too far in advance of other demes,or at least neighboring demes to accommodate migration.

Each of the deme managers can invoke their corresponding earlyconstructors in a manner similar to that depicted in FIG. 7. Each of thedeme managers maintains state of their deme, and requests earlyconstruction by their early constructor when a sub-population iseligible for early construction. If a deme manager is notified that theprocess is to be terminated or disrupted (e.g., resource reallocation,evolutionary termination criterion is satisfied, user intervention, acataclysm will be performed, etc.), the deme manager can interrupt theearly constructor. The deme managers can be configured to balance theefficiencies from out of order early construction of individuals againstthe risk of spending resources on individuals that will not beevaluated. For instance, the deme manager can be configured to requestearly construction less frequently or pause early construction if thedeme is far ahead of other demes or the evolutionary process is in lateriterations near the maximum number of runs/iterations.

The network of nodes 1000 depicted in FIG. 10 is an example and can takedifferent forms or configurations. For example, multiple nodes may carryout fitness evaluation. The network can be configured to assign afitness evaluator for a given number of demes, or even per deme.

While the embodiments are described with reference to variousimplementations and exploitations, it will be understood that theseembodiments are illustrative and that the scope of the inventive subjectmatter is not limited to them. In general, techniques for earlyconstruction of candidate solutions as described herein may beimplemented with facilities consistent with any hardware system orhardware systems. Many variations, modifications, additions, andimprovements are possible.

Plural instances may be provided for components, operations orstructures described herein as a single instance. Finally, boundariesbetween various components, operations and data stores are somewhatarbitrary, and particular operations are illustrated in the context ofspecific illustrative configurations. Other allocations of functionalityare envisioned and may fall within the scope of the inventive subjectmatter. In general, structures and functionality presented as separatecomponents in the example configurations may be implemented as acombined structure or component. Similarly, structures and functionalitypresented as a single component may be implemented as separatecomponents. These and other variations, modifications, additions, andimprovements may fall within the scope of the inventive subject matter.

What is claimed is:
 1. A method comprising: while at least one candidatesolution of a first generation of candidate solutions remains to beevaluated in accordance with a fitness function for an optimizationproblem, selecting a plurality of candidate solutions from the firstgeneration of candidate solutions to participate in a tournament,wherein in response to a first candidate solution of the plurality ofcandidate solutions being of an earlier generation in comparison to asecond candidate solution of the plurality of candidate solutions, thefirst candidate solution is given a higher priority than the secondcandidate solution that is used to prioritize selection; determiningthat each of the plurality of candidate solutions selected toparticipate in the tournament have been evaluated in accordance with thefitness function; selecting one or more winners of the tournament fromthe plurality of candidate solutions of the first generation ofcandidate solutions; and while at least one candidate solution of thefirst generation remains to be evaluated in accordance with the fitnessfunction, creating a candidate solution of a second generation ofcandidate solutions with the selected one or more winners of thetournament in accordance with a genetic operator.
 2. The method of claim1 further comprising: selecting a second plurality of candidatesolutions from the first generation of candidate solutions toparticipate in a second tournament; determining that at least one of thesecond plurality of candidate solutions selected to participate in thesecond tournament has not yet been evaluated in accordance with thefitness function; and waiting for an indication that the at least one ofthe second plurality of candidate solutions selected to participate inthe tournament has been evaluated in accordance with the fitnessfunction.
 3. The method of claim 1 further comprising: selecting asecond plurality of candidate solutions from the first generation ofcandidate solutions to participate in a second tournament; determiningthat at least one of the second plurality of candidate solutionsselected to participate in the second tournament has not yet beenevaluated in accordance with the fitness function; and beginning earlyconstruction of candidate solutions for a third generation of candidatesolutions, wherein the third generation is later than the secondgeneration.
 4. The method of claim 1 further comprising: computing aseed for a pseudo random number generator for each generation and deme,wherein the seed is unique across generations and demes; initializingthe pseudo random number generator when early construction of candidatesolutions begins or resumes for a generation; and if early constructionresumes for a given generation, then obtaining random numbers from thepseudo random number generator until returning to a state when atournament of the given generation could not be conducted.
 5. The methodof claim 1 further comprising: determining in advance the random numbersthat indicate the plurality of candidate solutions selected toparticipate in the tournament; and prioritizing fitness evaluation forthe plurality of candidate solutions of the first generation over othercandidate solutions of the first generation that are either not known toparticipate in any tournament or selected to participate in latertournaments.
 6. The method of claim 1 further comprising: computing aseed for a pseudo random number generator for each candidate solution,wherein the seed is unique across candidate solutions and acrossgenerations; initializing the pseudo random number generator when earlyconstruction of each candidate solution begins or resumes; and if earlyconstruction resumes for a given candidate solution, then obtainingrandom numbers from the pseudo random number generator until returningto a state when a tournament of the given candidate solution could notbe conducted.
 7. A computer program product for early construction ofcandidate solutions, the computer program product comprising: a computerreadable storage device having computer usable program code embodiedtherewith, the computer usable program code comprising a computer usableprogram code configured to: select a plurality of candidate solutionsfrom a first generation of candidate solutions to participate in atournament, wherein in response to a first candidate solution of theplurality of candidate solutions being of an earlier generation incomparison to a second candidate solution of the plurality of candidatesolutions, the first candidate solution is given a higher priority thanthe second candidate solution that is used to prioritize selection;determine whether each of the plurality of candidate solutions selectedto participate in the tournament have been evaluated in accordance withthe fitness function; select one or more winners of the tournament fromthe plurality of candidate solutions of the first generation ofcandidate solutions; and while at least one candidate solution of thefirst generation remains to be evaluated in accordance with the fitnessfunction, create a candidate solution of a second generation ofcandidate solutions with the selected one or more winners of thetournament in accordance with a genetic operator.
 8. The computerprogram product of claim 7 further comprising computer usable programcode configured to: select a second plurality of candidate solutionsfrom the first generation of candidate solutions to participate in asecond tournament; determine that at least one of the second pluralityof candidate solutions selected to participate in the second tournamenthas not yet been evaluated in accordance with the fitness function; andwait for an indication that the at least one of the second plurality ofcandidate solutions selected to participate in the tournament has beenevaluated in accordance with the fitness function.
 9. The computerprogram product of claim 7 further comprising computer usable programcode configured to: select a second plurality of candidate solutionsfrom the first generation of candidate solutions to participate in asecond tournament; determine that at least one of the second pluralityof candidate solutions selected to participate in the second tournamenthas not yet been evaluated in accordance with the fitness function; andbegin early construction of candidate solutions for a third generationof candidate solutions, wherein the third generation is later than thesecond generation.
 10. The computer program product of claim 7 furthercomprising computer usable program code configured to: compute a seedfor a pseudo random number generator for each generation and deme,wherein the seed is unique across generations and demes; initialize thepseudo random number generator when early construction of candidatesolutions begins or resumes for a generation; and if early constructionresumes for a given generation, then obtain random numbers from thepseudo random number generator until returning to a state when atournament of the given generation could not be conducted.
 11. Thecomputer program product of claim 7 further comprising computer usableprogram code configured to: determine in advance the random numbers thatindicate the plurality of candidate solutions selected to participate inthe tournament; and prioritize fitness evaluation for the plurality ofcandidate solutions of the first generation over other candidatesolutions of the first generation that are either not known toparticipate in any tournament or selected to participate in latertournaments.
 12. The computer program product of claim 7 furthercomprising computer usable program code configured to: compute a seedfor a pseudo random number generator for each candidate solution,wherein the seed is unique across candidate solutions and acrossgenerations; initialize the pseudo random number generator when earlyconstruction of each candidate solution begins or resumes; and if earlyconstruction resumes for a given candidate solution, then obtain randomnumbers from the pseudo random number generator until returning to astate when a tournament of the given candidate solution could not beconducted.
 13. An apparatus comprising: a processor; and a computerreadable storage medium coupled with the processor, the computerreadable storage medium having computer usable program code embodiedtherewith, the computer usable program code comprising a computer usableprogram code configured to: select a plurality of candidate solutionsfrom a first generation of candidate solutions to participate in atournament, wherein in response to a first candidate solution of theplurality of candidate solutions being of an earlier generation incomparison to a second candidate solution of the plurality of candidatesolutions, the first candidate solution is given a higher priority thanthe second candidate solution that is used to prioritize selection;determine whether each of the plurality of candidate solutions selectedto participate in the tournament have been evaluated in accordance withthe fitness function; select one or more winners of the tournament fromthe plurality of candidate solutions of the first generation ofcandidate solutions; and while at least one candidate solution of thefirst generation remains to be evaluated in accordance with the fitnessfunction, create a candidate solution of a second generation ofcandidate solutions with the selected one or more winners of thetournament in accordance with a genetic operator.
 14. The apparatus ofclaim 13 further comprising computer usable program code configured to:select a second plurality of candidate solutions from the firstgeneration of candidate solutions to participate in a second tournament;determine that at least one of the second plurality of candidatesolutions selected to participate in the second tournament has not yetbeen evaluated in accordance with the fitness function; and wait for anindication that the at least one of the second plurality of candidatesolutions selected to participate in the tournament has been evaluatedin accordance with the fitness function.
 15. The apparatus of claim 13further comprising computer usable program code configured to: select asecond plurality of candidate solutions from the first generation ofcandidate solutions to participate in a second tournament; determinethat at least one of the second plurality of candidate solutionsselected to participate in the second tournament has not yet beenevaluated in accordance with the fitness function; and begin earlyconstruction of candidate solutions for a third generation of candidatesolutions, wherein the third generation is later than the secondgeneration.
 16. The apparatus of claim 13 further comprising computerusable program code configured to: compute a seed for a pseudo randomnumber generator for each generation and deme, wherein the seed isunique across generations and demes; initialize the pseudo random numbergenerator when early construction of candidate solutions begins orresumes for a generation; and if early construction resumes for a givengeneration, then obtain random numbers from the pseudo random numbergenerator until returning to a state when a tournament of the givengeneration could not be conducted.
 17. The apparatus of claim 13 furthercomprising computer usable program code configured to: determine inadvance the random numbers that indicate the plurality of candidatesolutions selected to participate in the tournament; and prioritizefitness evaluation for the plurality of candidate solutions of the firstgeneration over other candidate solutions of the first generation thatare either not known to participate in any tournament or selected toparticipate in later tournaments.
 18. The apparatus of claim 13 furthercomprising computer usable program code configured to: compute a seedfor a pseudo random number generator for each candidate solution,wherein the seed is unique across candidate solutions and acrossgenerations; initialize the pseudo random number generator when earlyconstruction of each candidate solution begins or resumes; and if earlyconstruction resumes for a given candidate solution, then obtain randomnumbers from the pseudo random number generator until returning to astate when a tournament of the given candidate solution could not beconducted.